Project Summary/Abstract There is a tremendous need for tools that allow NIH sponsored investigators to easily share and disseminate data from brain imaging studies. Access to large neuroimaging data sets is a critical aspect of learning about how the brain works both in health and disease. Most typically, large data sets are collected by one investigator, used for a specific purpose, and then archived. Sometimes these data are shared with collaborators, typically after an extended effort. Such an approach takes a very narrow view of what could be learned from these rich data sets. For example, there are often multiple questions that could be asked but weren?t conceived at the time of the study design. There are also a plethora of advanced analytic techniques and approaches, developed by multiple groups, which can be applied to these data to identify hidden structure or answer new questions. However, there are many barriers to data sharing of multimodal neuroimaging data and the community has tried for years to confront the various barriers. In this direct Phase II SBIR we are proposing a unique approach which is to take the neuroinformatics tools that we have been developing at the Mind Research Network and create a user friendly neuroinformatics suite which will enable prospective management and sharing of studies, assessments, and neuroimaging data. Investigator tools allow management of privacy within prospective (ongoing) studies. These tools allow investigators to control what is shared with collaborators. We believe that robust privacy controls are essential for data sharing. All data is collected, stored, and managed within a scalable infrastructure. We will deliver our neuroinformatics suite to NIH sponsored investigators, as well as other customers and clients. The rollout process and implementation includes installation, support, training, and data storage. The successful completion of this project would represent a major transformation which could propel scientific sharing and knowledge extraction of diverse types of data into a practical and widely used model.